Consider the following unobserved effects model:Yit = ₁xit1 +...+ ßkXitk + Aį + UitYou plan on using fixed effects to estimate the model.Suppose you have a panel data set consisting of 10 individuals over 6 years. However, you are missing the last 3 years of data for the third, i = 3,individual in your data set.Your panel data set hastotal observations.Suppose the reason individual i is missing data for certain time periods (called attrition) is correlated with the idiosyncratic error, uit. Recall that ai isassumed to be uncorrelated with the idiosyncratic error, uit.This correlationcause the fixed effects estimators to be biased.

Answered: Consider the following unobserved…

MATLAB: An Introduction with Applications

6th Edition

ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

Chapter1: Starting With Matlab

Section: Chapter Questions

Problem 1P

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The condition of an independent variable being correlated to one or more other independent variables is referred to as nonlinearity. statistical significance. multicollinearity. linearity.
Beachcomer Ltd is a local car dealership that sells used and new vehicles. The manager of the company wants to know how different variables affect the sales of his vehicles. A random sample of yearly data was taken with the view to testing the model. SALES = a+BAGE + yMIL + SENG Where SALES = amount that a vehicle is sold for (000's), AGE = age of vehicle, MIL= the total mileage of the vehicle at the point of sale and ENG = the size of the engine. The sample of data was processed using MINITAB and the following is an extract of the output obtained: The regression equation is ***** Coef StDev t-ratio p-value Predictor Constant 1.7586 0.2525 6.9648 0.0000 AGE 0.2124 0.3175 * 0.5042 MIL -0.7527 0.3586 -2.0991 ** ENG 4.8124 0.6196 7.7664 0.0000…
Omitting a variable from our model that has a causal effect on our dependent variable always leads to omitted variable bias. a. False b. True
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10) The top point in the scatter plot (6.5, 1459) is for Washington, DC. Find the predicted DC violent crime rate if x=6.5. Then find the residual for DC using the predicted violence crime rate and the actual violent crime rate (the actual crime rate is provided in the first sentence of this question). Prediction = Actual y-value = Residual =
You plan to fit a regression model that will be used to predict first-year college GPA (FYGPA) from high-school GPA (HSGPA), ACT score (ACT), first-generation status (Yes or No), socioeconomic class (lower class, lower to middle class, middle to upper class, and upper class), and school type (public or private). What is the total number of estimated regression coefficients? If the sample size is n = 250 students, what are the degrees of freedom for the following sources of variation: Regression Error Total
Beachcomer Ltd is a local car dealership that sells used and new vehicles. The manager of the company wants to know how different variables affect the sales of his vehicles. A random sample of yearly data was taken with the view to testing the model. SALES = a+BAGE + yMIL + SENG Where SALES = amount that a vehicle is sold for (000's), AGE = age of vehicle, MIL= the total mileage of the vehicle at the point of sale and ENG = the size of the engine. The sample of data was processed using MINITAB and the following is an extract of the output obtained: The regression equation is ***** Coef StDev t-ratio p-value Predictor Constant 1.7586 0.2525 6.9648 0.0000 AGE 0.2124 0.3175 * 0.5042 MIL -0.7527 0.3586 -2.0991 ** ENG 4.8124 0.6196 7.7664 0.0000…
Beachcomer Ltd is a local car dealership that sells used and new vehicles. The manager of the company wants to know how different variables affect the sales of his vehicles. A random sample of yearly data was taken with the view to testing the model. SALES = a+BAGE + yMIL + SENG Where SALES = amount that a vehicle is sold for (000's), AGE = age of vehicle, MIL= the total mileage of the vehicle at the point of sale and ENG = the size of the engine. The sample of data was processed using MINITAB and the following is an extract of the output obtained: The regression equation is ***** Coef StDev t-ratio p-value Predictor Constant 1.7586 0.2525 6.9648 0.0000 AGE 0.2124 0.3175 * 0.5042 MIL -0.7527 0.3586 -2.0991 ** ENG 4.8124 0.6196 7.7664 0.0000…
Your state is going to vote on becoming a "right to work" state in the next, and you wonder if there is there a relationship between salary levels and unionization. You find data on salary from the U.S. Census, which is split into three groups: Low; Moderate/About Average, and; High. To see if salary, measured in this way, is associated with unionization you find a variable in the same dataset called "UNION", which has two categories: the respondent has a unionized job and the respondent does not have a unionized job. Because you want to know if you should favor or oppose becoming a "right to work" state, you test these two variables to see if there is an association between salary level and unionized labor. What type of hypothesis test is most appropriate?
Provide an example of a regression that arguably would have a high value of R² but would produce biased and inconsistent estimators of a causal effect. Explain why the R² is likely to be high. Explain why the OLS estimators would be biased and inconsistent.
The full question for the last question is written down below Fail/fail to reject H0. There is insufficient/sufficient evidence at the 10% level of significance to support the claim that there is a linear relationship between weight and number of hours slept.
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